[1]} studies independent policy gradient in the tabular case. To achieve last-iterate convergence, the two players have to use asymmetric learning rates, and only the one with a smaller learning rate converges to the minimax policy. In contrast, the two players of our algorithm are completely symmetric, and they simultaneously converge to the equilibrium set.
